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Zhongwei Li
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skills/ga4-bigquery/references/sql-query-cookbook.md
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skills/ga4-bigquery/references/sql-query-cookbook.md
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# GA4 BigQuery SQL Query Cookbook
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## Helper Functions (Use at Start of Queries)
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```sql
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-- String parameter extraction
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CREATE TEMP FUNCTION GetParam(params ANY TYPE, key STRING)
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RETURNS STRING AS (
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(SELECT value.string_value FROM UNNEST(params) WHERE key = key)
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);
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-- Integer parameter extraction
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CREATE TEMP FUNCTION GetParamInt(params ANY TYPE, key STRING)
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RETURNS INT64 AS (
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(SELECT value.int_value FROM UNNEST(params) WHERE key = key)
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);
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-- Float parameter extraction
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CREATE TEMP FUNCTION GetParamFloat(params ANY TYPE, key STRING)
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RETURNS FLOAT64 AS (
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(SELECT value.float_value FROM UNNEST(params) WHERE key = key)
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);
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-- Get any parameter type (returns as string)
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CREATE TEMP FUNCTION GetParamAny(params ANY TYPE, key STRING)
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RETURNS STRING AS (
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(SELECT COALESCE(
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value.string_value,
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CAST(value.int_value AS STRING),
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CAST(value.float_value AS STRING),
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CAST(value.double_value AS STRING)
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) FROM UNNEST(params) WHERE key = key)
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);
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```
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## Basic Queries
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### 1. Daily Active Users
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```sql
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SELECT
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event_date,
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COUNT(DISTINCT user_pseudo_id) as active_users
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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GROUP BY
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event_date
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ORDER BY
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event_date
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```
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### 2. Top Pages by Views
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```sql
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SELECT
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(SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'page_location') as page_location,
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COUNT(*) as page_views,
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COUNT(DISTINCT user_pseudo_id) as unique_users
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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AND event_name = 'page_view'
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GROUP BY
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page_location
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ORDER BY
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page_views DESC
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LIMIT 20
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```
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### 3. Session Count by Source/Medium
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```sql
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SELECT
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traffic_source.source,
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traffic_source.medium,
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COUNT(DISTINCT CONCAT(user_pseudo_id,
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(SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id')
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)) as sessions,
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COUNT(DISTINCT user_pseudo_id) as users
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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GROUP BY
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traffic_source.source,
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traffic_source.medium
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ORDER BY
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sessions DESC
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```
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## E-commerce Queries
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### 4. Revenue by Date
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```sql
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SELECT
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event_date,
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COUNT(DISTINCT ecommerce.transaction_id) as transactions,
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COUNT(DISTINCT user_pseudo_id) as purchasers,
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SUM(ecommerce.purchase_revenue_in_usd) as revenue,
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AVG(ecommerce.purchase_revenue_in_usd) as avg_order_value
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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AND event_name = 'purchase'
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AND ecommerce.transaction_id IS NOT NULL
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GROUP BY
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event_date
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ORDER BY
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event_date
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```
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### 5. Top Selling Products
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```sql
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SELECT
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item.item_name,
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item.item_category,
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SUM(item.quantity) as units_sold,
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SUM(item.item_revenue_in_usd) as total_revenue,
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COUNT(DISTINCT ecommerce.transaction_id) as transactions
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FROM
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`project.dataset.events_*`,
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UNNEST(items) as item
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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AND event_name = 'purchase'
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GROUP BY
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item.item_name,
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item.item_category
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ORDER BY
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total_revenue DESC
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LIMIT 20
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```
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### 6. Conversion Funnel Analysis
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```sql
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WITH funnel AS (
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SELECT
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user_pseudo_id,
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MAX(IF(event_name = 'view_item_list', 1, 0)) as viewed_list,
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MAX(IF(event_name = 'view_item', 1, 0)) as viewed_item,
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MAX(IF(event_name = 'add_to_cart', 1, 0)) as added_cart,
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MAX(IF(event_name = 'begin_checkout', 1, 0)) as began_checkout,
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MAX(IF(event_name = 'purchase', 1, 0)) as purchased
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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GROUP BY
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user_pseudo_id
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)
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SELECT
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SUM(viewed_list) as step1_viewed_list,
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SUM(viewed_item) as step2_viewed_item,
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SUM(added_cart) as step3_added_cart,
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SUM(began_checkout) as step4_began_checkout,
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SUM(purchased) as step5_purchased,
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-- Conversion rates
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ROUND(SUM(viewed_item) / SUM(viewed_list) * 100, 2) as pct_list_to_item,
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ROUND(SUM(added_cart) / SUM(viewed_item) * 100, 2) as pct_item_to_cart,
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ROUND(SUM(began_checkout) / SUM(added_cart) * 100, 2) as pct_cart_to_checkout,
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ROUND(SUM(purchased) / SUM(began_checkout) * 100, 2) as pct_checkout_to_purchase,
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ROUND(SUM(purchased) / SUM(viewed_list) * 100, 2) as overall_conversion_rate
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FROM
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funnel
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```
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### 7. Cart Abandonment Rate
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```sql
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SELECT
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event_date,
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COUNT(DISTINCT IF(event_name = 'add_to_cart', user_pseudo_id, NULL)) as users_added_cart,
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COUNT(DISTINCT IF(event_name = 'purchase', user_pseudo_id, NULL)) as users_purchased,
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ROUND((1 - COUNT(DISTINCT IF(event_name = 'purchase', user_pseudo_id, NULL)) /
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COUNT(DISTINCT IF(event_name = 'add_to_cart', user_pseudo_id, NULL))) * 100, 2) as abandonment_rate_pct
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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AND event_name IN ('add_to_cart', 'purchase')
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GROUP BY
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event_date
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ORDER BY
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event_date
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```
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## User Behavior Queries
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### 8. User Journey (Event Sequence)
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```sql
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SELECT
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user_pseudo_id,
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ARRAY_AGG(
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STRUCT(
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event_timestamp,
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event_name,
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(SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'page_location') as page
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)
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ORDER BY event_timestamp
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) as journey
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX = '20250115'
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AND user_pseudo_id = 'USER_ID_HERE'
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GROUP BY
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user_pseudo_id
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```
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### 9. Session Duration Distribution
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```sql
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WITH sessions AS (
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SELECT
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CONCAT(user_pseudo_id, '-',
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(SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id')
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) as session_id,
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MAX(event_timestamp) - MIN(event_timestamp) as session_duration_micros
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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GROUP BY
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session_id
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)
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SELECT
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CASE
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WHEN session_duration_micros < 10000000 THEN '0-10 sec'
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WHEN session_duration_micros < 30000000 THEN '10-30 sec'
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WHEN session_duration_micros < 60000000 THEN '30-60 sec'
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WHEN session_duration_micros < 180000000 THEN '1-3 min'
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WHEN session_duration_micros < 600000000 THEN '3-10 min'
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ELSE '10+ min'
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END as duration_bucket,
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COUNT(*) as session_count
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FROM
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sessions
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GROUP BY
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duration_bucket
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ORDER BY
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MIN(session_duration_micros)
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```
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### 10. New vs Returning Users
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```sql
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WITH first_visits AS (
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SELECT
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user_pseudo_id,
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MIN(event_timestamp) as first_visit_timestamp
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20240101' AND '20250131'
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GROUP BY
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user_pseudo_id
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)
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SELECT
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event_date,
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COUNT(DISTINCT IF(TIMESTAMP_MICROS(event_timestamp) = TIMESTAMP_MICROS(fv.first_visit_timestamp),
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e.user_pseudo_id, NULL)) as new_users,
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COUNT(DISTINCT IF(TIMESTAMP_MICROS(event_timestamp) > TIMESTAMP_MICROS(fv.first_visit_timestamp),
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e.user_pseudo_id, NULL)) as returning_users
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FROM
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`project.dataset.events_*` e
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LEFT JOIN
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first_visits fv
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ON
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e.user_pseudo_id = fv.user_pseudo_id
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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GROUP BY
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event_date
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ORDER BY
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event_date
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```
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## Attribution Queries
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### 11. First Touch Attribution
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```sql
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WITH first_touch AS (
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SELECT
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user_pseudo_id,
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ARRAY_AGG(
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STRUCT(
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traffic_source.source,
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traffic_source.medium,
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traffic_source.name as campaign
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)
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ORDER BY event_timestamp LIMIT 1
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)[OFFSET(0)] as first_source
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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AND traffic_source.source IS NOT NULL
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GROUP BY
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user_pseudo_id
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),
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purchases AS (
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SELECT
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user_pseudo_id,
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COUNT(DISTINCT ecommerce.transaction_id) as purchases,
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SUM(ecommerce.purchase_revenue_in_usd) as revenue
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FROM
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`project.dataset.events_*`
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WHERE
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
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AND event_name = 'purchase'
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GROUP BY
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user_pseudo_id
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)
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SELECT
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ft.first_source.source,
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ft.first_source.medium,
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ft.first_source.campaign,
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COUNT(DISTINCT ft.user_pseudo_id) as users,
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SUM(p.purchases) as total_purchases,
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SUM(p.revenue) as total_revenue
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FROM
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first_touch ft
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LEFT JOIN
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purchases p
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ON
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ft.user_pseudo_id = p.user_pseudo_id
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GROUP BY
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ft.first_source.source,
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ft.first_source.medium,
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ft.first_source.campaign
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ORDER BY
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total_revenue DESC
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```
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### 12. Last Touch Attribution
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```sql
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WITH last_touch AS (
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SELECT
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ecommerce.transaction_id,
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ARRAY_AGG(
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STRUCT(
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traffic_source.source,
|
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traffic_source.medium
|
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)
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ORDER BY event_timestamp DESC LIMIT 1
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)[OFFSET(0)] as last_source,
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SUM(ecommerce.purchase_revenue_in_usd) as revenue
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FROM
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`project.dataset.events_*`
|
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WHERE
|
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
|
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AND event_name = 'purchase'
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GROUP BY
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ecommerce.transaction_id
|
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)
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SELECT
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last_source.source,
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last_source.medium,
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COUNT(DISTINCT transaction_id) as conversions,
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SUM(revenue) as total_revenue
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FROM
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last_touch
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GROUP BY
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last_source.source,
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last_source.medium
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ORDER BY
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total_revenue DESC
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```
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## Device and Technology
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||||
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### 13. Device Category Performance
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```sql
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SELECT
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device.category as device_category,
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COUNT(DISTINCT user_pseudo_id) as users,
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COUNT(DISTINCT CONCAT(user_pseudo_id,
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(SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id')
|
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)) as sessions,
|
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COUNTIF(event_name = 'purchase') as purchases,
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SUM(IF(event_name = 'purchase', ecommerce.purchase_revenue_in_usd, 0)) as revenue,
|
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ROUND(COUNTIF(event_name = 'purchase') / COUNT(DISTINCT CONCAT(user_pseudo_id,
|
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(SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id')
|
||||
)) * 100, 2) as conversion_rate_pct
|
||||
FROM
|
||||
`project.dataset.events_*`
|
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WHERE
|
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_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
|
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GROUP BY
|
||||
device.category
|
||||
ORDER BY
|
||||
users DESC
|
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```
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|
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### 14. Browser and OS Analysis
|
||||
|
||||
```sql
|
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SELECT
|
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device.browser,
|
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device.operating_system,
|
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COUNT(DISTINCT user_pseudo_id) as users,
|
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AVG((SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'engagement_time_msec')) / 1000 as avg_engagement_sec
|
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FROM
|
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`project.dataset.events_*`
|
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WHERE
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||||
_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
|
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AND event_name = 'user_engagement'
|
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GROUP BY
|
||||
device.browser,
|
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device.operating_system
|
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HAVING
|
||||
users > 100
|
||||
ORDER BY
|
||||
users DESC
|
||||
```
|
||||
|
||||
## Cohort and Retention
|
||||
|
||||
### 15. Weekly Cohort Retention
|
||||
|
||||
```sql
|
||||
WITH cohorts AS (
|
||||
SELECT
|
||||
user_pseudo_id,
|
||||
FORMAT_DATE('%Y-W%V', PARSE_DATE('%Y%m%d', MIN(event_date))) as cohort_week
|
||||
FROM
|
||||
`project.dataset.events_*`
|
||||
WHERE
|
||||
_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
|
||||
GROUP BY
|
||||
user_pseudo_id
|
||||
),
|
||||
activity AS (
|
||||
SELECT
|
||||
user_pseudo_id,
|
||||
FORMAT_DATE('%Y-W%V', PARSE_DATE('%Y%m%d', event_date)) as activity_week
|
||||
FROM
|
||||
`project.dataset.events_*`
|
||||
WHERE
|
||||
_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
|
||||
GROUP BY
|
||||
user_pseudo_id,
|
||||
activity_week
|
||||
)
|
||||
SELECT
|
||||
c.cohort_week,
|
||||
a.activity_week,
|
||||
COUNT(DISTINCT c.user_pseudo_id) as cohort_size,
|
||||
COUNT(DISTINCT a.user_pseudo_id) as active_users,
|
||||
ROUND(COUNT(DISTINCT a.user_pseudo_id) / COUNT(DISTINCT c.user_pseudo_id) * 100, 2) as retention_pct
|
||||
FROM
|
||||
cohorts c
|
||||
LEFT JOIN
|
||||
activity a
|
||||
ON
|
||||
c.user_pseudo_id = a.user_pseudo_id
|
||||
GROUP BY
|
||||
c.cohort_week,
|
||||
a.activity_week
|
||||
ORDER BY
|
||||
c.cohort_week,
|
||||
a.activity_week
|
||||
```
|
||||
|
||||
## Custom Dimensions and Parameters
|
||||
|
||||
### 16. Query Custom Event Parameters
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
event_name,
|
||||
(SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'custom_parameter') as custom_value,
|
||||
COUNT(*) as event_count
|
||||
FROM
|
||||
`project.dataset.events_*`
|
||||
WHERE
|
||||
_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
|
||||
AND event_name = 'custom_event'
|
||||
GROUP BY
|
||||
event_name,
|
||||
custom_value
|
||||
ORDER BY
|
||||
event_count DESC
|
||||
```
|
||||
|
||||
### 17. User Properties Analysis
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
(SELECT value.string_value FROM UNNEST(user_properties) WHERE key = 'user_tier') as user_tier,
|
||||
COUNT(DISTINCT user_pseudo_id) as users,
|
||||
SUM(IF(event_name = 'purchase', ecommerce.purchase_revenue_in_usd, 0)) as total_revenue,
|
||||
SUM(IF(event_name = 'purchase', ecommerce.purchase_revenue_in_usd, 0)) /
|
||||
COUNT(DISTINCT user_pseudo_id) as revenue_per_user
|
||||
FROM
|
||||
`project.dataset.events_*`
|
||||
WHERE
|
||||
_TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
|
||||
GROUP BY
|
||||
user_tier
|
||||
ORDER BY
|
||||
total_revenue DESC
|
||||
```
|
||||
|
||||
## Performance Tips
|
||||
|
||||
1. **Always use _TABLE_SUFFIX filtering** (not event_date)
|
||||
2. **Filter on clustered columns** (event_name, event_timestamp)
|
||||
3. **Select only needed columns**
|
||||
4. **Use LIMIT during development**
|
||||
5. **Create helper functions** for repeated UNNEST operations
|
||||
6. **Avoid SELECT *** unless necessary
|
||||
7. **Use materialized views** for frequently run queries
|
||||
8. **Monitor query costs** in BigQuery console
|
||||
Reference in New Issue
Block a user